DSC 241: Statistical Models; 4 units:

Linear/nonlinear models, diagnostics, polynomial regression, robust methods, regularization and penalization (ridge regression, lasso, etc.), bootstrap, model selection (cross-validation), generalized linear models, nonparametric regression, linear classification, classification and regression trees, boosting, neural networks.

Prerequisites: DSC 210, 212

Recommended Preparation: Basic programming or prior exposure to programming language such as R, Python, Matlab, etc.

DSC 260: Data Ethics and Fairness; 4 units:

Data science is transforming our lives, and so we must consider the ethical and societal impacts, implications, and constraints in our data science efforts. This course will consider foundational concepts including power, justice, bias, privacy, and explainability; societal practices including delegation, organizational incentives, and accountability; and governance mechanisms including law, regulation, and norms.

DSC 204A: Scalable Data Systems; 4 units:

Storage/memory hierarchy, distributed scalable computing (cluster, cloud, edge) principles. Big Data storage, management and processing at scale. Dataflow programming systems and programming models (MapReduce/Hadoop and Spark).

Prerequisites: DSC 202

DSC 204B: Big Data Analytics Using Spark; 4 units:

This course is a hands-on introduction to big data analytics. Topics covered include: I/O bottleneck and the memory hierarchy; HDFS and Spark; RMS error minimization, PCA and percent of variance explained. Analysis of NOAA weather data. Data collection and curation. Limitations of train/test methodology and leaderboards. Kmeans and intrinsic dimension. Classification, Boosting and XGBoost. Margins. Neural Networks and tensorflow. Students will develop the skills and attitudes required to write jupyter notebooks that can be understood by domain experts.

Prerequisites: DSC 200, 210, 212

Cross-listing: CSE 255

DSC 206: Algorithms for Data Science; 4 units:

With the advent of large-scale machine learning, online social networks, and computationally intensive models, data scientists must deal with data that is massive in size, arrives fast, and must be processed within an interactive or online manner. This course studies the mathematical foundations of massive data processing, developing algorithms and analyzing them. We explore methods for sampling, sketching, and distributed processing of large scale databases, clustering, dimensionality reduction, and methods of optimization for the purpose of scalable statistical description, querying, pattern mining, and learning from data.

Prerequisites: DSC 212

DSC 215: Statistical Thinking & Experimental Design; 4 units:

We hold science in high regard, however, not all scientific claims are correct. How do we know which claims to trust and which not to? This fundamental question is at the heart of this course. The goal of this course is to enable the student to evaluate any paper in data science, regardless of application area. Topics covered include experimental design, claims, evidence and statistical significance, The Replication Crisis, falsifiability, philosophy of science, history of probability and statistics. About half of thee class meetings, as well as the final project, would be devoted to evaluating contemporary papers in data science. This class will be in the form of an open discussion, based on provided reading materials. The only prerequisite is a statistics class that covered hypothesis testing and P-values.

Recommended Preparation: Hypothesis testing and p-values, basic statistics.

DSC 242: High-dimensional Probability and Statistics; 4 units:

Concentration inequalities, Markov processes and ergodicity, martingale inequalities, empirical processes, sparse linear models in high dimensions, principal component analysis in high dimensions, estimation of large covariance matrices.

Recommended Preparation: Undergraduate probability theory

DSC 243: Advanced Optimization; 4 units:

Linear/quadratic programming, optimization under constraints, optimization on the space of probabilities, gradient descent (deterministic and stochastic), convergence rate of gradient descent, acceleration phenomena in convex optimization, stochastic optimization with large data sets, complexity lower bounds for convex.

Prerequisites: DSC 211, DSC 212

DSC 244: Large-Scale Statistical Analysis; 4 units:

Large-scale hypothesis testing, Family-wise error rate (FWER) and false discovery rate (FDR) control and estimation, empirical null distribution, empirical covariance matrices, empirical Bayes methods.

Prerequisites: DSC 210, DSC 212, DSC 241

DSC 245: Introduction to Causal Inference; 4 units:

Causal versus predictive inference, potential outcomes and randomized experiments (A/B testing), structural causal models (interventions, counterfactuals, causal diagram, do-operator, d-separation), causal structure learning (constraint and score-based algorithms, and functional causal model-based methods), identification of causal effect (back-door and front-door criterion, do-calculus), estimation of causal effect (matching, propensity score, doubly robust estimation, instrumental variables, conditional effects), advanced topics (causal discovery and inference in the presence of distribution shifts, selection bias, hidden confounders, cycles, nonlinear causal mechanisms, missing values, and causal representation learning.)

Prerequisites: DSC 212, DSC 240

Recommended Preparation: Basic statistics and machine learning.

DSC 250: Advanced Data Mining; 4 units:

Graph mining and basic text analysis (including keyphrase extraction and generation), set expansion and taxonomy construction, graph representation learning, graph convolutional neural networks, heterogeneous information networks, label propagation, and truth findings.

Recommended Preparation: Knowledge about Machine Learning and Data Mining, coding with python, C/C++, Java; statistics

DSC 261: Responsible Data Science; 4 units:

Data Science lifecycle, data cleaning and quality management, data profiling, causal inference, algorithmic fairness (fairness definitions, impossibility results, causal fairness, building fair ML models, fairness beyond classification), algorithmic transparency (interpretability vs explainability, auditing-black-box algorithms, algorithmic recourse).

Prerequisites: DSC 210, DSC 240

Recommended Preparation: Machine learning, causal inference, data management